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LinkedIn Learning

Hands-On AI: RAG using LlamaIndex

via LinkedIn Learning

Overview

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Learn how to enhance AI query capabilities and data accuracy through the application of LlamaIndex in retrieval-augmented generation processes.

Syllabus

Introduction
  • Overcome the limitations of LLMs with RAG
  • Limitations of LLMs
  • Use cases for retrieval-augmented generation (RAG)
1. Getting Started
  • Using GitHub Codespaces
  • Setting up your environment
  • Choosing an LLM and embeddings provider
  • Setting up LLM accounts
  • Choosing a vector database
  • Setting up a Qdrant account
  • Downloading our data
2. Fundamental Concepts in LlamaIndex
  • How LlamaIndex is organized
  • Using LLMs
  • Loading data
  • Indexing
  • Storing and retrieving
  • Querying
  • Agents
3. Introduction to RAG
  • Components of a RAG system
  • Ingestion pipeline
  • Query pipeline
  • Prompt engineering for RAG
  • Data preparation for RAG
  • Putting it all together
  • Drawbacks of Naive RAG
4. RAG Evaluation
  • Introduction to RAG evaluation
  • Evaluation metrics
  • How to create an evaluation set
5. Advanced RAG: Pre-Retrieval and Indexing Techniques
  • How we can improve on Naive RAG
  • Optimizing chunk size
  • Small to big retrieval
  • Semantic chunking
  • Metadata extraction
  • Document summary index
  • Query transformation
6. Advanced RAG: Post-Retrieval and Other Techniques
  • Node post-processing
  • Re-ranking
  • FLARE
  • Prompt compression
  • Self-correcting
7. Modular RAG
  • Hybrid retrieval
  • Agentic RAG
  • Ensemble retrieval
  • Ensemble query engine
Conclusion
  • LlamaIndex evaluation
  • Comparative analysis of retrieval-augmented generation techniques

Taught by

Harpreet Sahota

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